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Low-Order Dominant Harmonic Estimation Using Adaptive Wavelet Neural Network

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2 Author(s)
Jain, S.K. ; PDPM Indian Inst. of Inf. Technol., Design & Manuf., Jabalpur, India ; Singh, S.N.

In recent years, harmonic pollution has worried the power engineers considerably due to the increased penetration of power-electronics-based devices in the utility grid. Monitoring of certain low-order harmonics in the power supply is more important than monitoring of the entire spectrum because, usually, these are the most significant ones. In this paper, a technique based on an adaptive wavelet neural network that is the most suitable for dominant low-order harmonic estimation is presented. The proposed method works with only half-cycle data point inputs, compared to the requirement of at least one-complete-cycle data for other estimation techniques. A simple, fast converging, and reliable learning algorithm based on back propagation is used for training of the network parameters. The proposed method is examined with a number of simulated and experimental signals. The test results confirm that the proposed method accurately estimates the dominant low-order harmonics in pragmatic situations of fundamental frequency deviation, presence of interharmonics, low signal-to-noise ratio, etc.

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Industrial Electronics, IEEE Transactions on  (Volume:61 ,  Issue: 1 )